Recently, the widespread use of smart devices for Internet of Things has led to a massive growth in data and information on the World Wide Web. However, multiple sources from the Web often provide conflicting descriptions for the same objects, thereby complicating the task of truth discovery, especially when sources may copy information from others. Existing approaches typically neglect the dependence and accuracy of these sources, resulting in low accuracy and efficiency in truth discovery. To solve the problem, we proposeDepenBaye, a source-dependent truth discovery framework, which incorporates Bayesian probability, the Simulated Annealing method, and the expectation maximization (EM) method. By evaluating the copy probability of sources based on false claims, reliable sources are identified with a simulated annealing method to improve efficiency. According to the EM method, source reliability and claim confidence are iteratively calculated to discover the latent truth.DepenBaye’s performance has been validated through extensive experiments, which outperforms existing approaches in efficiency and effectiveness.

In the era of Big Data and generative artificial intelligence (AI), discovering the truth about various objects from different sources has become a pressing topic. Existing studies primarily focus on dependent sources with conflicting information, where sources may copy information from each other. However, real-world scenarios are often more complex, with dynamic dependence relationships among sources over time. This complexity makes it much more difficult to discover the truth. One of the key challenges centers on measuring the dynamic dependence among sources. To address this challenge, we have developed three models:$Depen\_{S}imple$,$Depen\_{C}omplex$, and$Depen\_{D}ynamic$. These models are based on the Hidden Markov Model (HMM) and are designed to handle different types of dependencies, namelysimple source dependence,complex source dependence, anddynamic source dependence. Based on the constructed models, we propose a generic framework for discovering the latent truth which are evaluated by three HMM-based methods. We conduct extensive experiments on three real-world datasets to evaluate the performance of the proposed methods, and the results demonstrate that all three methods achieve high accuracy over the state-of-the-art methods.

Vehicular Edge Metaverse (VEM) is a new paradise supported by the Internet of Things, AI, and wireless communication technologies which provide various Virtual Vehicle Services (VVSs), where users can immerse and enjoy their spiritual world. To provide various VVSs for users, there is a significant increase in computational demands. The limited computing resource available on the vehicles are insufficient to handle the massive volume of tasks and the diverse needs of users. Edge nodes could provide more services than vehicles but longer transmission time and the cloud node can provide more services than edge nodes with longer transmission time. To provide better experiences for users, we use a three-layer (cloud-edge-vehicles) resource framework in VEM. In this paper, we construct a VEM offloading framework with communication and computation capabilities for metaverse services. It considers tasks with different requirements and comprehensively evaluates offloading decisions and resource allocation to maximize user's satisfaction in the metaverse and minimize the energy consumption of vehicles. To achieve this, a two-stage offloading algorithm based on a hybrid heuristic approach is proposed, aiming to find the Pareto optimal solution for the bi-objective optimization problem. Finally, experiments demonstrate that the proposed algorithm over-performs other algorithms with real dataset, validating that the proposed algorithm can effectively enhance user service satisfaction and reduce energy consumption.

To advance the United Nations Sustainable Development Goal on promoting sustained, inclusive, and sustainable economic growth, foreign direct investment (FDI) plays a crucial role in catalyzing economic expansion and fostering innovation. Precise city-level FDI prediction is quite important for local government and is commonly studied based on economic data (e.g., GDP). However, such economic data could be prone to manipulation, making predictions less reliable. To address this issue, we try to leverage large-scale judicial data which reflects judicial performance influencing local investment security and returns, for city-level FDI prediction. Based on this, we first build an index system for the evaluation of judicial performance over twelve million publicly available adjudication documents according to which a tabular dataset is reformulated. We then propose a new Tabular Learning method on Judicial Data (TLJD) for city-level FDI prediction. TLJD integrates row data and column data in our built tabular dataset for judicial performance indicator encoding, and utilizes a mixture of experts model to adjust the weights of different indicators considering regional variations. To validate the effectiveness of TLJD, we design cross-city and cross-time tasks for city-level FDI predictions. Extensive experiments on both tasks demonstrate the superiority of TLJD (reach to at least 0.92 R2) over the other ten state-of-the-art baselines in different evaluation metrics.

In the era of data information explosion, there are different observations on an object (e.g., the height of the Himalayas) from different sources on the web, social sensing, crowd sensing, and data sensing applications. Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc. How to discover truth facts for objects from various sources is essential and urgent. In this paper, we aim to deliver a comprehensive and exhaustive survey on truth discovery problems from the perspectives of concepts, methods, applications, and opportunities. We first systematically review and compare problems from objects, sources, and observations. Based on these problem properties, different methods are analyzed and compared in depth from observation with single or multiple values, independent or dependent sources, static or dynamic sources, and supervised or unsupervised learning, followed by the surveyed applications in various scenarios. For future studies in truth discovery fields, we summarize the code sources and datasets used in above methods. Finally, we point out the potential challenges and opportunities on truth discovery, with the goal of shedding light and promoting further investigation in this area.

Worldwide, Geo-distributed Data Centers (GDCs) provide computing and storage services for massive workflow applications, resulting in high electricity costs that vary depending on geographical locations and time. How to reduce electricity costs while satisfying the deadline constraints of workflow applications is important in GDCs, which is determined by the execution time of servers, power, and electricity price. Determining the completion time of workflows with different server frequencies can be challenging, especially in scenarios with heterogeneous computing resources in GDCs. Moreover, the electricity price is also different in geographical locations and may change dynamically. To address these challenges, we develop a geo-distributed system architecture and propose an Electricity Cost aware Multiple Workflows Scheduling algorithm (ECMWS) for servers of GDCs with fixed frequency and power. ECMWS comprises four stages, namely workflow sequencing, deadline partitioning, task sequencing, and resource allocation where two graph embedding models and a policy network are constructed to solve the Markov Decision Process (MDP). After statistically calibrating parameters and algorithm components over a comprehensive set of workflow instances, the proposed algorithms are compared with the state-of-the-art methods over two types of workflow instances. The experimental results demonstrate that our proposed algorithm significantly outperforms other algorithms, achieving an improvement of over 15% while maintaining an acceptable computational time. The source codes are available athttps://gitee.com/public-artifacts/ecmws-experiments.

Multi-access edge computing (MEC) is an emerging computing paradigm that brings services from the centralized cloud to nearby network edge to improve users’ Quality of Experience (QoE). As massive services with dynamic Quality of Service (QoS) are available in MEC, it becomes challenging for users to find reliable services that satisfy their needs. Therefore, service recommendation technology is urgently needed in MEC. Although existing service recommendation methods work well on recommending popular services that users might be interested in, they fail to recommend services with reliable QoS in the MEC environment. To tackle this issue, an accurate and reliable service recommendation (ARSR) approach based on bilateral perception is proposed, which aims to proactively recommend reliable services by perceiving both users’ service demands and multi-QoS of candidate services. ARSR consists of three main steps. First, a user's service demand is estimated by a context-aware service demand prediction method based on an improved online deep learning model. Then, multiple QoS attributes of candidate services are forecasted by a multidimensional contexts-aware QoS prediction method based on an improved multi-task deep neural network. Finally, the optimal service is recommended to the user based on the predicted QoS. Extensive experiments have been carried out to verify the proposed approach and to prove its performance superiority.

Sleep stage recognition is crucial for assessing sleep and diagnosing chronic diseases. Deep learning models, such as Convolutional Neural Networks and Recurrent Neural Networks, are trained using grid data as input, making them not capable of learning relationships in non-Euclidean spaces. Graph-based deep models have been developed to address this issue when investigating the external relationship of electrode signals across different brain regions. However, the models cannot solve problems related to the internal relationships between segments of electrode signals within a specific brain region. In this study, we propose a Pearson correlation-based graph attention network, called PearNet, as a solution to this problem. Graph nodes are generated based on the spatial-temporal features extracted by a hierarchical feature extraction method, and then the graph structure is learned adaptively to build node connections. Based on our experiments on the Sleep-EDF-20 and Sleep-EDF-78 datasets, PearNet performs better than the state-of-the-art baselines.

Performance analysis and optimization is a critical task for the successful development of cloud computing systems and services. Unfortunately, performance analysis and optimization remains complicated and challenging due to several unique characteristics in cloud computing such as stochastic service requests, request sequencing strategies, and request distribution methods. In this paper, we present a comprehensive survey on the performance analysis and optimization for stochastic cloud service requests. By analyzing the main entities and activities in the common routines of performance analysis, we first propose a generic performance analysis framework, which contains five fundamental characteristics: Request, Sequencing, Queue, Distribution and Services. Practical factors of each characteristic are analyzed. We discuss the effects of each characteristic of the framework on optimization objectives including cost, profit, response time, and energy consumption. We then systematically review and compare 13 representative queuing models using the proposed framework. Based on the practical factors of the five characteristics and along with the current research efforts, we also identify several research opportunities and challenges.

Influence maximization, i.e., the problem of finding a small subset of nodes in a social network which can maximize the propagation of influence, has the potential to become a vital asset to identify and predict mental health related issues such as, predicting suicide, bulling, and radicalization. For example, predictive analytics in mental health can enable analyzing and exploring the factors involved in influencing people to participate in extremist activities. To address this challenge, in this paper, we analyze the influence maximization in mental health from effectiveness, efficiency, and scalability viewpoints. We present a social data analytics pipeline to enable analysts to engage with social data to explore the potential online radicalization. According to the predictive analytics, a particle swarm optimization influence maximization algorithm is proposed to facilitate selecting potential influential nodes. A context analytics algorithm is proposed to analyze the social data and the user activity patterns to learn how influence flows in social networks. We conducted intensive experiments based on real dataset and illustrate the effectiveness and efficiency of the proposed algorithms.

In cloud computing, service providers rent heterogeneous servers from cloud providers, i.e., Infrastructure as a Service (IaaS), to meet requests of consumers. The heterogeneity of servers and impatience of consumers pose great challenges to service providers for profit maximization. In this article, we transform this problem into a multi-queue model where the optimal expected response time of each queue is theoretically analyzed. A multi-queue request scheduling algorithm framework is proposed to maximize the total profit of service providers, which consists of three components: request stream splitting, requests allocation, and server assignment. A request stream splitting algorithm is designed to split the arriving requests to minimize the response time in the multi-queue system. An allocation algorithm, which adopts a one-step improvement strategy, is developed to further optimize the response time of the requests. Furthermore, an algorithm is developed to determine the appropriate number of required servers of each queue. After statistically calibrating parameters and algorithm components over a comprehensive set of random instances, the proposed algorithms are compared with the state-of-the-art over both simulated and real-world instances. The results indicate that the proposed multi-queue request scheduling algorithm outperforms the other algorithms with acceptable computational time.

The rapid development of mobile Internet services and the wide application of intelligent terminals has accelerated the advent of the promising era of big data. A number of big data services based on location information bring convenience to users, however, it also results in serious leakage of personal privacy. The partitioning and publishing method combined with the differential privacy model can provide better range counting query results under the premise of ensuring the privacy of users' location. Nevertheless, most of the existing research studies only focus on the structural design during the partitioning process of location big data and ignore the impact of differential privacy budget allocation methods on the published results. This paper, therefore, proposes an efficient arithmetic privacy budget allocation strategy for the tree-based partitioning and publishing of location big data which satisfies the ε-differential privacy. Experimental results over a large number of real-world datasets prove that the proposed privacy budget allocation method is superior in contrast to the existing methods for improving the usability of the published data.

In this article, we consider the problem of selecting appropriate heterogeneous servers in cloud centers for stochastically arriving requests in order to obtain an optimal tradeoff between the expected response time and power consumption. Heterogeneous servers with uncertain setup times are far more common than homogenous ones. The heterogeneity of servers and stochastic requests pose great challenges in relation to the tradeoff between the two conflicting objectives. Using the Markov decision process, the expected response time of requests is analyzed in terms of a given number of available candidate servers. For a given system availability, a binary search method is presented to determine the number of servers selected from the candidates. An iterative improvement method is proposed to determine the best servers to select for the considered objectives. After evaluating the performance of the system parameters on the performance of algorithms using the analysis of variance, the proposed algorithm and three of its variants are compared over a large number of random and real instances. The results indicate that proposed algorithm is much more effective than the other four algorithms within acceptable CPU times.

In this paper, we consider the cost minimization problem for scheduling stochastic service requests to heterogenous servers in cloud computing. Service requests are impatient with different maximizing waiting time. Using queuing theory, a queuing system model is constructed. An algorithm framework is proposed to minimize the cost. The actual expected waiting time of service requests is analyzed. The rejection probability of the system is obtained. Comparing the rejection probability of the system to a given system availability, suitable servers are selected to minimize the cost. Based on the proposed framework, algorithms with different components are compared. Experimental results show that the algorithm with the mixed server selection strategy outperforms the others on efficiency.